Monday, July 04, 2016

Could AI replace teachers? 10 ways it could?

Teachers are not ends-in-themselves, they are always a means
to an end - improvements in the learner. Given this premise, could it be
possible to eventually replace teachers with AI technology? This may not happen
soon but let’s, as a thought experiment, ask whether it could.

Obvious points are that AI is 24/7, fast, scalable and
cheaper. This gives it a head start. But could it teach? First, we need to
break down the functions of teaching and learning. I have used a PGSE schema as
my starting point, supplemented by other learning tasks.

1. Searching for
answers

Google is nothing but AI and has already delivered a
remarkable pedagogic shift with search. Let’s also reflect on something that
has already happened. Since Google’s inception, the number of librarians,
teachers of sorts, has steadily fallen. We have less need of book and Journal
warehouses, now that most knowledge is online. Beyond this, OER resources, such
as Wikipedia, YouTube and Khan Academy have transformed the landscape. All of
this is available through AI enabled search. Beyond search we see apps, such as
Photomaths, where you simply point your smartphone at a maths problem and
it gives you the answer. More than this, it gives you the steps in between.
That’s how powerful AI has become.

2. Student support

We have a good example of student support in the remarkable
Georgia Tech bot, Jill Watson. This AI teaching assistant bot, trained to answer based on prior
responses, was used with 300 AI students, without them knowing. They
were more than fooled, they praised the teacher for its efficacy and speed. In
fact, it was only because it was so fast in its replies that they spotted it
was a bot. We can expect a lot more of this, as teacher support, gives way to intelligent
AI agent support. Teachers, especially online teachers, spend a lot of time on
admin and support, so this is one area that is ripe for AI automation.

3. Demonstrate good
subject and curriculum knowledge

This is where AI has made good progress and could outperform
teachers. At one level Google already provides access to ‘knowledge’ on every
subject, in detail. On ‘knowledge’ Watson beat the Jeopardy Champions way back
in 2011. It is now a web-based service with access to huge knowledge bases and
AI. At the skills level, YouTube is already the search engine of
choice for learning how to ‘do’ things. With 3D, virtual worlds one can see how
learn by doing can be expanded, as it was with flight sims, through cheap
consumer technology, high in AI. The Todai project in Japan has produced AI
software that passed the Tokyo University entrance exam, in Maths, Physics,
English and History with a score of 53.8% versus the average human score of
human 43.8%. Why is this important? Well, if AI has graduate level skills at
this stage, it is likely to replace graduate-level jobs.

4. Plan and teach
well-structured lessons

Lessons or learning experiences can be idiosyncratic, even
flawed. AI design offers, not only optimal design but also continuous
improvement, as it uses individual and aggregated data to spot poor components
in lessons. There are plenty of examples of this already, from MOOCs and other
forms of online learning, where errors in content, bad designed questions,
overlong videos and presentations, have all been caught by data produced by
online learning systems that constantly look for improvement. ‘Structured’ is
an interesting concept here and one could argue that learning needs to be far
more structured than the one-size-fits-all lesson plan. Differentiation could
be identified and handled by AI in a way that traditional teaching cannot. The
promise is of learning experiences that are not only structured towards
individual learners but also continuously improve as machine learning identifies
and acts on identified weaknesses. AI may even automatically produce lessons and content. See WildFire.

5. Promote good
progress and outcomes by pupils

Progress tracking is not easy, as it requires the
simultaneous tracking of actual performance across many learners. This is
notoriously difficult in teaching. AI, on the other hand, promises to do this
across many learners in realtime, as it gathers evidence that no teacher can
possibly hope to gather through traditional observation and testing. More than
this, one could argue that AI has a lot to offer in being free from human
biases, that sometimes inhibit learner progress. We have ample evidence of
gender and socio-economic bias in teaching. AI can be free from bias on gender,
race, accent and background. Again, AI promises a scalable solution to this
problem that may be of higher quality than a teacher-delivered system.

6. Adapt teaching to
respond to the strengths and needs of all pupils

One of AI’s first forays into teaching has been through
adaptive systems. These are already at work and producing impressive results. They act like a Satnav, which constantly monitors the performance of individual
learners and adjusts what they are asked to do next. This is done in realtime.
Content is no longer a linear curriculum of flat resources but a network of
learning experiences that can be dynamically delivered to individual learners,
based on their precise needs at that precise time. The analytic and predictive
strengths of AI may very well identify factors that both inhibit and enhance
learning in any individual.

In addition, AI can monitor and deliver to the needs of all
pupils, especially those with special educational needs; high ability, English
as an additional language, disabilities, and be able to use and improve
distinctive approaches. Technology has already made a big difference in SEN, AI
will make an ever bigger difference. All of this may be way beyond the ability
of any teacher to deliver on scale. It is here that the first successes in AI
driven teaching are being achieved.

Chris Piech’s team at Stanford & Google, used 1.4m answers to problems set by Khan
Academy to diagnose what any learner was likely to get right/wrong in their
next question. The software had to know what went wrong and why. It is, currently, accurate 85% of the time.Why is this important? Because that automated
skill is better than the average teacher.

7. Make accurate and
productive use of assessment

If we break assessment down into formative and summative,
both, it can be argued, could benefit from AI.

Formative assessment is difficult and often of poor quality
in one to many classrooms. It is largely absent from the lecture hall. There
are three ways that AI could improve formative assessment, in ways superior to
current ‘teacher’ delivery. First the quantity; adaptive learning systems,
could deliver more feedback than teachers. It is self-evident that AI is
scalable in the way a teacher is not and can deliver millions of pieces of
feedback to millions of learners in milliseconds – this is what Google does
already, albeit at a basic level. Second, AI could deliver higher quality
feedback, which can also be used to determine what is literally delivered next
in an online lesson. Formative assessment is one area where AI already excels.
Increasingly we will also see, through AI, immediate feedback, delivered
verbally or in text, as AI driven speech recognition and delivery becomes
commonplace. With speech we will move towards the sort of frictionless
interface than enables good teaching and learning.

On summative assessment, AI can deliver adaptive questioning
and, using Item Response Theory, deliver assessment that includes learner
confidence and other data during the assessment that no teacher could gather.
It can also deliver to whatever statutory assessment requirements are in place.
Essay marking is reaching a level, where it can perform as good as an expert assessor.
Automated marking is also becoming more common. Online proctoring uses AI in
typing patterns to identify the examinees, as is face recognition for digital
identity and realtime face recognition, as the learner takes the exam.

Assessment is clearly one area where AI has, and will continue to make
inroads. If teachers no longer have to set, and mark, homework, assignments and
exams, a huge portion of the teaching task will have been automated.

8. Set high
expectations which inspire, motivate and challenge pupils

This is tricky, as it is difficult to measure. No one would
argue that technology is not motivating, even compelling, especially for the
young. Whether it is motivating and compelling for learning is another
question. With the emergence of the Smartphone, gamification, AR, VR and
frictionless speech recognition, we already see signs of technology that is
both powerful in terms of learning and compelling. Gamification certainly tackles
the motivational issue. AR offers layered experiences. VR offers inspiring,
complete immersion and attention, emotional pull, learning by doing,
contextualised learning and high retention (witness flight sims for pilots). It
promises to democratise something teachers in classrooms cannot offer -
experiences. AI brings all sorts of additional dimensions to learning. Many
aspects of good ‘learning theory’ are now being embedded in AI and used for
teaching. I argue elsewhere, that we in the learning game, have a lot to learn
from AI, as it starts to distinguish what is good from bad in learning theory.

9. Manage behaviour
effectively

We could envisage a future where adult supervision of
learning does not need highly qualified, subject-trained teachers but
facilitators. We can even imagine the transformation of schools, into places
where one learns, not places where teachers teach. This is a radical shift but
as teaching becomes automated so schools become places of learning, not
teaching. In practice this pendulum swing has started. There are plenty of
online learning courses and degrees out there and learners are starting to do
it for themselves. This can only continue. One argument is that it will
accelerate, now that AI is in the field, as it has in other areas of human
endeavour.

10. High standards of
personal and professional conduct

One can hardly ask this of AI but perhaps it becomes less of
a problem. However, the modelling that teachers provide is certainly important
to young people. Teachers provide values and models of conduct that one hopes
are emulated. Again however, we may see the development of attitudinal
learning, that was never adequately delivered in classrooms, with simulations
and the ability to put yourself in the shoes of others. AI is already feeding the
VR industry with intelligent avatars, which are commonly used in games but
increasingly in attitudinal learning. You become the bullied person, the
subject of racism, sexism or bigotry.

Conclusion

Few saw self-driving, autonomous cars coming. That happened
because of AI. Few may also see self-driving, autonomous learners. That may
also come through AI. Machine learning not only embodies learning, it learns
about learners while they learn. It is like a fast learning teacher. There is
every reason to suppose that teaching may, to some degree, even a large degree,
be automated by AI. If this is possible, it will be a momentous breakthrough,
as education at all levels, whether schools, colleges, universities or
workplaces, will experience massive reductions in costs. The developing world,
where teaching is a problem in terms of quality and supply, is also likely to
receive a huge boost. At some point we may look back at teachers and classrooms
as we look back on manual manufacturing in factories. I’m not suggesting that teachers
are in any way not valuable or smart, just that AI technology may, as in many
other areas, get more valuable and smarter.